Mechanical Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2795
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Item Equilibrium Programming for Improved Management of Water-Resource Systems(2024) Boyd, Nathan Tyler; Gabriel, Steven A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Effective water-resources management requires the joint consideration of multiple decision-makers as well as the physical flow of water in both built and natural environments. Traditionally, game-theory models were developed to explain the interactions of water decision-makers such as states, cities, industries, and regulators. These models account for socio-economic factors such as water supply and demand. However, they often lack insight into how water or pollution should be physically managed with respect to overland flow, streams, reservoirs, and infrastructure. Conversely, optimization-based models have accounted for these physical features but usually assume a single decision-maker who acts as a central planner. Equilibrium programming, which was developed in the field of operations research, provides a solution to this modeling dilemma. First, it can incorporate the optimization problems of multiple decision-makers into a single model. Second, the socio-economic interactions of these decision-makers can be modeled as well such as a market for balancing water supply and demand. Equilibrium programming has been widely applied to energy problems, but a few recent works have begun to explore applications in water-resource systems. These works model water-allocation markets subject to the flow of water supply from upstream to downstream as well as the nexus of water-quality management with energy markets. This dissertation applies equilibrium programming to a broader set of physical characteristics and socio-economic interactions than these recent works. Chapter 2 also focuses on the flow of water from upstream to downstream but incorporates markets for water recycling and reuse. Chapter 3 also focuses on water-quality management but uses a credit market to implement water-pollution regulations in a globally optimal manner. Chapter 4 explores alternative conceptions for socio-economic interactions beyond market-based approaches. Specifically, social learning is modeled as a means to lower the cost of water-treatment technologies. This dissertation's research contributions are significant to both the operations research community and the water-resources community. For the operations research community, this dissertation could serve as model archetypes for future research into equilibrium programming and water-resource systems. For instance, Chapter 1 organizes the research in this dissertation in terms of three themes: stream, land, and sea. For the water-resources community, this dissertation could make equilibrium programming more relevant in practice. Chapter 2 applies equilibrium programming to the Duck River Watershed (Tennessee, USA), and Chapter 3 applies it to the Anacostia River Watershed (Washington DC and Maryland, USA). The results also reinforce the importance of the relationships between socio-economic interactions and physical features in water resource systems. However, the risk aversion of the players acts as an important mediating role in the significance of these relationships. Future research could investigate mechanisms for the emergence of altruistic decision-making to improve equity among the players in water-resource systems.Item THREE ESSAYS ON OPTIMIZATION, MACHINE LEARNING, AND GAME THEORY IN ENERGY(2023) Chanpiwat, Pattanun; Gabriel, Steven A.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation comprises three main essays that share a common theme: developing methods to promote sustainable and renewable energy from both the supply and demand sides, from an application perspective. The first essay (Chapter 2) addresses demand response (DR) scheduling using dynamic programming (DP) and customer classification. The goal is to analyze and cluster residential households into homogeneous groups based on their electricity load. This allows retail electric providers (REPs) to reduce energy use and financial risks during peak demand periods. Compared to a business-as-usual heuristic, the proposed approach has an average 2.3% improvement in profitability and runs approximately 70 times faster by avoiding the need to run the DR dynamic programming separately for each household. The second essay in Chapter 3 analyzes the integration of renewable energy sources and battery storage in energy systems. It develops a stochastic mixed complementarity problem (MCP) for analyzing oligopolistic generation with battery storage, taking into account both conventional and variable renewable energy supplies. This contribution is novel because it considers multi-stage stochastic MCPs with recourse decisions. The sensitivity analysis shows that increasing battery capacity can reduce price volatility and variance of power generation. However, it has a small impact on carbon emissions reduction. Using a stochastic MCP approach can increase power producers' profits by almost 20 percent, as proposed by the value of stochastic equilibrium solutions. Higher battery storage capacity reduces the uncertainty of the system in all cases related to average delivered prices. Nevertheless, investing in enlarging battery storage has diminishing returns to producers' profits at a certain point restricted by market limitations such as demand and supply or pricing structure. The third essay (Chapter 4) proposes a new practical application of the stochastic dual dynamic programming (SDDP) algorithm that considers uncertainties in the electricity market, such as electricity prices, residential photovoltaic (PV) generation, and loads. The SDDP model optimizes the scheduling of battery storage usage for sequential decision-making over a planning horizon by considering predicted uncertainty scenarios and their associated probabilities. After examining the benefits of shared battery storage in housing companies, the results show that the SDDP model improves the average objective function values (i.e., costs) by approximately 32% compared to a model without it. The results also indicate that the mean objective function values at the end of the first stage of the proposed SDDP model with battery storage and the deterministic LP model equivalent (with perfect foresight) with battery storage differ by less than 30%. The models and insights developed in this dissertation are valuable for facilitating energy policy-making in a rapidly evolving industry. Furthermore, these contributions can advance computational techniques, encourage the use and development of renewable energy sources, and increase public education on energy efficiency and environmental awareness.Item Mixed Complementarity Modeling in the Global Natural Gas Market(2020) Huemme, Justin; Gabriel, Steven A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis describes the development and capabilities of the 2020 World Gas Model (WGM), an updated mixed complementarity problem model of the global natural gas market derived from the 2014 World Gas Model. The significance of this research applies to industry professionals and academics alike as the developed processes and analysis further expands the capabilities and flexibility of equilibrium modeling. Through an understanding of the current state of the natural gas market, the WGM determines the economic behavior of various market players with the deployment of Karush-Kuhn-Tucker (KKT) optimality conditions in conjunction with market-clearing conditions. The capabilities of the World Gas Model are highlighted through two case studies that are of varying international importance. The case studies are specifically selected from different issues that face the natural gas market such as a United States and China trade war and U.S. Coast Guard liquefied natural gas (LNG) inspection workforce forecasting. The goal of the United States and China Trade War case study is to analyze the potential long- and short-term affects of a prolonged trade war under several different possible scenarios. Results from the study indicate that while increased tariffs on LNG trade from the U.S. to China greatly reduce the amount of trade volume between the two countries, the overall economic effect is negligible and of greater concern to other affected nations. Another found result, is that if the potential geopolitical consequence of China increasing their domestic production of natural gas in an effort to reduce reliance on imports, this will cause a global natural gas market effect. The U.S. Coast Guard LNG inspection workforce forecasting case study utilizes the WGM to provide the future workforce demand for U.S. regulatory personnel and the associated costs based on the growth of the U.S. LNG industry. The results from the study indicate that in order to avoid future costs and restriction on the U.S. LNG industry, the USCG must increase its LNG inspection workforce by a factor of .3 to 1 from current forecasts.Item MODELING AND SIMULATION OF NOVEL MEDICAL RESPONSE SYSTEMS FOR OUT-OF-HOSPITAL CARDIAC ARREST(2020) Lancaster, Greg James; Herrmann, Jeffrey W; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sudden Cardiac Arrest (SCA) is the leading cause of death in the United States, resulting in 350,000 deaths annually. SCA survival requires immediate medical treatment with a defibrillatory shock and cardiopulmonary resuscitation. The fatality rate for out-of-hospital cardiac arrest is 90%, due in part to the reliance on Emergency Medical Services (EMS) to provide treatment. A substantial improvement in survival could be realized by applying early defibrillation to cardiac arrest victims. Automated External Defibrillators (AEDs) allow lay rescuers to provide early defibrillation, before the arrival of EMS. However, very few out-of-hospital cardiac arrests are currently treated with AEDs. Novel response concepts are being explored to reduce the time to defibrillation. These concepts include mobile citizen responders dispatched by a cell phone app to nearby cardiac arrest locations, and the use of drones to deliver AEDs to a cardiac arrest scene. A small number of pilot studies of these systems are currently in progress, however, the effectiveness of these systems remains largely unknown. This research presents a modeling and simulation approach to predict the effectiveness of various response concepts, with comparison to the existing standard of EMS response. The model uses a geospatial Monte Carlo sampling approach to simulate the random locations of a cardiac arrest within a geographical region, as well as both random and fixed origin locations of responding agents. The model predicts response time of EMS, mobile dispatched responders, or drone AED delivery, based on the distance travelled and the mode of transit, while accounting for additional system factors such as dispatch time, availability of equipment, and the reliability of the responders. Response times are translated to a likelihood of survival for each simulated case using a logistic regression model. Sensitivity analysis and response surface designed experiments were performed to characterize the important factors for response time predictions. Simulations of multiple types of systems in an example region are used to compare potential survival improvements. Finally, a cost analysis of the different systems is presented along with a decision analysis approach, which demonstrates how the method can be applied based on the needs and budgets of a municipality.Item COMBINED ROBUST OPTIMAL DESIGN, PATH AND MOTION PLANNING FOR UNMANNED AERIAL VEHICLE SYSTEMS SUBJECT TO UNCERTAINTY(2019) Rudnick-Cohen, Eliot; Azarm, Shapour; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Unmanned system performance depends heavily on both how the system is planned to be operated and the design of the unmanned system, both of which can be heavily impacted by uncertainty. This dissertation presents methods for simultaneously optimizing both of these aspects of an unmanned system when subject to uncertainty. This simultaneous optimization under uncertainty of unmanned system design and planning is demonstrated in the context of optimizing the design and flight path of an unmanned aerial vehicle (UAV) subject to an unknown set of wind conditions. This dissertation explores optimizing the path of the UAV down to the level of determining flight trajectories accounting for the UAVs dynamics (motion planning) while simultaneously optimizing design. Uncertainty is considered from the robust (no probability distribution known) standpoint, with the capability to account for a general set of uncertain parameters that affects the UAVs performance. New methods are investigated for solving motion planning problems for UAVs, which are applied to the problem of mitigating the risk posed by UAVs flying over inhabited areas. A new approach to solving robust optimization problems is developed, which uses a combination of random sampling and worst case analysis. The new robust optimization approach is shown to efficiently solve robust optimization problems, even when existing robust optimization methods would fail. A new approach for robust optimal motion planning that considers a “black-box” uncertainty model is developed based off the new robust optimization approach. The new robust motion planning approach is shown to perform better under uncertainty than methods which do not use a “black-box” uncertainty model. A new method is developed for solving design and path planning optimization problems for unmanned systems with discrete (graph-based) path representations, which is then extended to work on motion planning problems. This design and motion planning approach is used within the new robust optimization approach to solve a robust design and motion planning optimization problem for a UAV. Results are presented comparing these methods against a design study using a DOE, which show that the proposed methods can be less computationally expensive than existing methods for design and motion planning problems.Item OPTIMAL SCHEDULING OF RESIDENTIAL DEMAND RESPONSE USING DYNAMIC PROGRAMMING(2019) Moglen, Rachel Lee; Gabriel, Steven A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Electricity price volatility in the Electricity Reliability Council of Texas (ERCOT) poses significant financial threat to many players in the electricity market, though most of the financial burden falls on the retail electric providers (REPs). REPs are contractually obligated to purchase and provide all electricity that the end-user wishes to consume, even when the purchase of this electricity brings them financial losses. Electricity prices in ERCOT can increase from typical ranges ($30/MWh) to $3,000/MWh in as little as 15 minutes, causing REPs to seek mitigation techniques to avoid paying price spike prices for electricity. We explore two techniques for REPs to schedule mitigation techniques: a price prediction-based heuristic approach, as well as an optimal scheduling algorithm using dynamic programming (DP). We aim to optimally schedule these mitigation techniques which shift load from high price periods to times of lower prices, called demand response (DR). To achieve this load shifting, REPs remotely manipulate internet-connected thermostats of residential customers, thereby controlling a fraction of residential HVAC load. We found that the price prediction approach was highly unreliable, even for predicting prices as near as 5 minutes out. We therefore chose to rely on the DP as the primary scheduling model. By applying the DP deterministically to historical electricity price and weather data, the load-shifting technique is shown to potentially improve REP profit margins by 10% to 25% per customer annually. Most of these savings come from a few crucial events, highlighting the usefulness of the DP and the importance of accuracy in the timing of DR events. Due to the uncertainty in electricity prices, we apply a multi-objective approach considering the REP’s conflicting objectives: maximizing savings and minimizing financial risk. Results from this multi-objective formulation point to shorter duration DR events in the evening being the least risky, with additional savings possible through riskier short midday events. To ensure that REPs could apply our DP formulation for use in near real-time decision-making applications, the computation speed was verified to be under one second for 24 stages (i.e., 1-hour intervals for one day.)Item Optimal Budget-Constrained Sample Allocation for Selection Decisions with Multiple Uncertain Attributes(2016) Leber, Dennis D.; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A decision-maker, when faced with a limited and fixed budget to collect data in support of a multiple attribute selection decision, must decide how many samples to observe from each alternative and attribute. This allocation decision is of particular importance when the information gained leads to uncertain estimates of the attribute values as with sample data collected from observations such as measurements, experimental evaluations, or simulation runs. For example, when the U.S. Department of Homeland Security must decide upon a radiation detection system to acquire, a number of performance attributes are of interest and must be measured in order to characterize each of the considered systems. We identified and evaluated several approaches to incorporate the uncertainty in the attribute value estimates into a normative model for a multiple attribute selection decision. Assuming an additive multiple attribute value model, we demonstrated the idea of propagating the attribute value uncertainty and describing the decision values for each alternative as probability distributions. These distributions were used to select an alternative. With the goal of maximizing the probability of correct selection we developed and evaluated, under several different sets of assumptions, procedures to allocate the fixed experimental budget across the multiple attributes and alternatives. Through a series of simulation studies, we compared the performance of these allocation procedures to the simple, but common, allocation procedure that distributed the sample budget equally across the alternatives and attributes. We found the allocation procedures that were developed based on the inclusion of decision-maker knowledge, such as knowledge of the decision model, outperformed those that neglected such information. Beginning with general knowledge of the attribute values provided by Bayesian prior distributions, and updating this knowledge with each observed sample, the sequential allocation procedure performed particularly well. These observations demonstrate that managing projects focused on a selection decision so that the decision modeling and the experimental planning are done jointly, rather than in isolation, can improve the overall selection results.Item Electronic Part Total Cost Of Ownership And Sourcing Decisions For Long Life Cycle Products(2011) Prabhakar, Varun Jonathan; Sandborn, Peter A.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The manufacture and support of long life cycle products rely on the availability of suitable parts from competent suppliers which, over long periods of time, leaves parts susceptible to a number of possible long-term supply chain disruptions. Potential supply chain failures can be supplier-related (e.g., bankruptcy, changes in manufacturing process, non-compliance), parts-related (e.g., obsolescence, reliability, design changes), logistical (e.g., transportation mishaps, natural disasters, accidental occurrences) and political/legislative (e.g., trade regulations, embargo, national conflict). Solutions to mitigating the risk of supply chain failure include the strategic formulation of suitable part sourcing strategies. Sourcing strategies refer to the selection of a set of suppliers from which to purchase parts; sourcing strategies include sole, single, dual, second and multi-sourcing. Utilizing various sourcing strategies offer one way of offsetting or avoiding the risk of part unavailability (and its associated penalties) as well as possible benefits from competitive pricing. Although supply chain risks and sourcing strategies have been extensively studied for high-volume, short life cycle products, the applicability of existing work to long life cycle products is unknown. Existing methods used to study part sourcing decisions in high-volume consumer oriented applications are procurement-centric where cost tradeoffs on the part level focus on part pricing, negotiation practices and purchase volumes. These studies are commonplace for strategic part management for short life cycle products; however, conventional procurement approaches offer only a limited view for parts used in long life cycle products. Procurement-driven decision making provides little to no insight into the accumulation of life cycle cost (attributed to the adoption, use and support of the part), which can be significantly larger than procurement costs in long life cycle products. This dissertation defines the sourcing constraints imposed by the shortage of suppliers as a part becomes obsolete or is subject to other long-term supply chain disruptions. A life cycle approach is presented to compare the total cost of ownership of introducing and supporting a set of suppliers, for electronic parts in long life cycle products, against the benefit of reduced long-term supply chain disruption risk. The estimation of risk combines the likelihood or probability of long-term supply chain disruptions (throughout the part's procurement and support life within an OEM's product portfolio) with the consequence of the disruption (impact on the part's total cost of ownership) to determine the "expected cost" associated with a particular sourcing strategy. This dissertation focuses on comparing sourcing strategies used in long life cycle systems and provides application-specific insight into the cost benefits of sourcing strategies towards proactively mitigating DMSMS type part obsolescence.